{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qlseAUvJJDXM"
   },
   "source": [
    "## The Data\n",
    "\n",
    "** Source: https://datamarket.com/data/set/22ox/monthly-milk-production-pounds-per-cow-jan-62-dec-75#!ds=22ox&display=line **\n",
    "\n",
    "**Monthly milk production: pounds per cow. Jan 62 - Dec 75**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5WTUlbh4JDXN"
   },
   "source": [
    "** Import numpy pandas and matplotlib **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Xg2peEzGJDXO"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hBtEtgN6JDXS"
   },
   "source": [
    "** Use pandas to read the csv of the monthly-milk-production.csv file and set index_col='Month' **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "FPJdTW0TJcMA",
    "outputId": "787910c9-8137-4f65-bc4a-1295e793ce81"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive \n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "nArYXHMPJDXT"
   },
   "outputs": [],
   "source": [
    "milk = pd.read_csv(\"gdrive/My Drive/dataML/monthly-milk-production.csv\",index_col='Month')\n",
    "\n",
    "#milk = pd.read_csv(\"monthly-milk-production.csv\",index_col='Month')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "RYvb00fEJDXW",
    "outputId": "d108e603-eb49-47de-da17-95bef20f46dd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01 01:00:00</th>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01 01:00:00</th>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01 01:00:00</th>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01 01:00:00</th>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01 01:00:00</th>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production\n",
       "Month                               \n",
       "1962-01-01 01:00:00            589.0\n",
       "1962-02-01 01:00:00            561.0\n",
       "1962-03-01 01:00:00            640.0\n",
       "1962-04-01 01:00:00            656.0\n",
       "1962-05-01 01:00:00            727.0"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qd6qU_zOJDXb"
   },
   "source": [
    "** Check out the head of the dataframe**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Z0aPxAkWJDXg"
   },
   "source": [
    "** Make the index a time series by using: **\n",
    "\n",
    "    milk.index = pd.to_datetime(milk.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ej2PnSz7JDXh"
   },
   "outputs": [],
   "source": [
    "milk.index = pd.to_datetime(milk.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "colab_type": "code",
    "id": "TuNcJhMoJDXj",
    "outputId": "3ebdeb74-d924-45c5-dec4-807a7adec1da"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1962-01-01 01:00:00</th>\n",
       "      <td>589.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-02-01 01:00:00</th>\n",
       "      <td>561.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-03-01 01:00:00</th>\n",
       "      <td>640.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-04-01 01:00:00</th>\n",
       "      <td>656.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1962-05-01 01:00:00</th>\n",
       "      <td>727.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production\n",
       "Month                               \n",
       "1962-01-01 01:00:00            589.0\n",
       "1962-02-01 01:00:00            561.0\n",
       "1962-03-01 01:00:00            640.0\n",
       "1962-04-01 01:00:00            656.0\n",
       "1962-05-01 01:00:00            727.0"
      ]
     },
     "execution_count": 6,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "-rgkKTs6JDXn",
    "outputId": "bf0fca39-729a-43e0-cc9e-b198fb9c4d1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Milk Production'], dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "milk.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z8Hk38WeJDXq"
   },
   "source": [
    "** Plot out the time series data. **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "id": "qXNiSsZUJDXq",
    "outputId": "7197a065-660d-457c-aae7-442372e35cf1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f64b32bb1d0>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PoVyh+MAVtcPLmYXV6cnULgxtxlJKv0spvZxSei2AFQBHAcwzS0b5uqA8fRpyxM8YU+6r\nX/NeSukeSumegYGBTn4GDoejg9ZHJoRgoKezCDmZk/Dj/dN4964R9Gl6ufd4XQh4nB1ZNy9OrKC/\nx1vTIz4WFDqyQh55bQ4VCtyys4nQh6z1u6lUKP7xxTO4akuvOkJQPd41lktvNOsmpnzdCNmf/58A\nHgJwp/KUOwH8TLn9EIA7lOybqwAkNRYPh8NZBbQDNwAlQu4gov+nfWeQl8q465rxhsc62YgsVyj+\n/egirr9gAA5NFkss5MVi2vrYv4dfncWW/gC2DwcbHhsKCVjMFCCZrLx99sQyJuN5fPDKjQ2Psfz8\nmcQ6EnoAPyaEvA7g5wA+QSlNAPg6gHcSQo4BeIfyPQA8DOAkgOMA/geA37P3kDkcjlmSdfnuA0HB\ncmZMuUJx/7OnceV4b9OBHbLnba3b5MtnVpDMS7jhgljN/bGgF6UKtTT2j9k2t+wcbvDn5eP1gVLz\nNsvjRxbgdTnwazuGGh4bibBe9/Z03ewUQxOmKKVvb3LfMoCbmtxPAXyi80PjcDh20Sj0XhyYXLG0\n1sRyFtOJPD5109amjw+HfXj2xJKltR8/sgCngzRsbsaC1aEmZsf+PX5kERUK3LyzUZABYChcbULG\nBNoI2YK87yG4G6dphQQ3eryudRfRczicc8RypoBP/M+XkFSyWuwgJTYK/XK2aKlRWK4g52L0BpoL\n7nBY9tOtrP34G4u4fFO05lgBTX6+hQ1Z1i54U1+g6eOsOtasTy9K5aYizxju4MrGbrjQczhrjH2n\nV/CLg7N47qS1qLgeqVxBrliubVUQ9FpuFJYtylWvAU9zkRsKCyhXKJYy5taeS4p4fTbVYNvIx2u9\nOlaU5BOO4Goud2ykoHmhr0Bwt5bQ4YiPR/QcDqc5rGDo2Lw9rYTre7EAUNsgWEn/yys9cnwthH4k\nomxEmoxmX59NAgCuGI82PBbrIF1RLJXhchC4nM3lLup3w+NymN5AFkvtI/rRCI/oORxOC1hPluOL\n9gp9vXUDWLNC1Ije23yLbyhkLULOF+XIu0doXFdwOxEUXJY2kPUsFkIIBkNe00VT+WIZgquddePD\nUmZtFE1xoedw1his4vT4gj1CzwS3r6fakybWQUSfYxF9C/FUI/qEuWhWlOR1W4mn1UlTehYLYG0A\niViqwNvOugnbN1qxU7jQczhrDBaBn1jMWM4b1/LiRByEALuUebEA1MwVK0VTzLrxt7Bu5EwUh3nP\nu9TeErJaNFWQyvC2ibwBa9WxBZ0rhVElg2ct+PRc6DmcDth/Om660EYPJvSiVMG0yai4GS9OxLF9\nKFTTnldwOxH2uS0VTelZN4QQjIR9pq2Q6qZpC6EPeS0VYsleul5EL1s3ZoaxiFK55VUNIG/GAuav\nbM4GXOg5HIvMJPL4D99+Dj99uaHDR0ck8pLa27xT+6ZYqmD/6RVcubm34TG5f4y1iJ4Q1HSBrMdK\n0RSzblrZIcy6MTsZS5QqLa8SGIMhAcVSRbXNjK7bNusmzHrdc6HncNYtS0o0/PpMytZ1k3kJFw2H\nAHQu9IdmkhClCt7SROitbEACQLZQRsDjalplyhi2ENEXpPYnECbGbMi3UUSp/aYpoEmxNHHFoJd1\nI7id6A14MMM9eg5n/cIsFrs2TRmpvIRNfX7093g6XnvvqTgAYM94o9CPRnyWrKG8VNKNkK0UTYml\nCrwuR8sTiNVMIb2sGwCWevQbWXckImCWWzcczvqFRZZH59O2rpvIFRHxu3HeQE/HKZZ7T8WxZSCg\niqSWsagfi+mCapkYJVcstyyWYgxHzBdN6Qmn1aIpI1k3vQE5IymeNbY2pVRet419BchXCnwzlsNZ\nx7DRdAvpgm3tCiilSIlyD5WtsR4cm0+b9qQZlQrFvol4U9sGAMai1jYLs4UyfJ72bbLU7o0m/Gm9\nvPSYxTYIYqkMr07k3a+0c1g2eGIqlOQrFb11R8KC6cKxswEXes6bgv/znw/h+ZPLtq7JrBsAOLpg\nT1SfKZRQrlCEfXJEnxJLltoUAPKkppRYwvmDja15ATmiB4CpFXNClJdKLVMrGVbaCoil9pumsaC1\nlNCCVNH16EM+F1wOYvizVnP+dYR+OOJDWuxsWLodcKHndD2iVMb3nz+Nn78yY+u6KY3Q29WuQB3P\n5/OohUdWC25EJeoMtIi+WURvVuizhbIBoTdfNCVK5baZPD1eF3xupwXrRj+9khCC3oAHcYMRPUsF\nbZdeCWiLplY3qudCz+l6mCCfiefsXVeUEPW74fc4bfPptX1prHZVZLDCJqGFKA+GBLgcBNMJc59L\nvqgv9Kxoykzeu5FWBVZy6Y1smgKyT79s0KOvRvT6FbcAMJdc3ZGChvrRczjrmYQinqeXbRb6fAkR\nvwdBwYVjNlk3KU1fGnX+qsVZqdWWAs3FyOkgGI4I5iP6YqnlVQKDEGI6T79gYNPUShsEsaS/LiC3\niDBs3ZSMWTfsd2i2vYLd8Iie0/WwKHk6kbfUI73duiHBhW2xII7a3Gky4ndjoMcLBwHmrVo3UvuW\nAgAwFvGb9+iLZd30SgAY6DE3rlAvLx2QZ7GaOXlI5QrKFarr0QNAX8CLuGGPXqni1TmBDK6R2bFc\n6DldD8uIKVeoraluKVEeuH3+YA8W0wUkLIy5qyehiehdTgf6e7yWo8G8gQ3DsagPUyvmrnRyBqwb\nwHzlrZHCpljQa6qDpdFNU0Cxbgx79O0bsDEEtxMRv3vVG5txoed0PQnNpunpeNa2deWB225sG+wB\nIDch65T6lsJDYQFzFhqPAZq+8e0ab0V9WEgXDLfSrVQo8lIZfh3rBrAi9EasGwHZYhlZg1ksRiNv\nAOgLeJAplAx9FtV2DfonkFadMYulCh7ce8aW/zd6cKHndD3aNEg7ffpkvoSQz632X7fSCbJxTQlu\nJ1Ej5lhQsG7dlJjItYvo/aAUmDV4pcOuEoxE9LGggJWchGLJmF2WN1LBGjRXwWpGkPuUjp5G7Bsz\nJ5DBkNA0ol/KFPDFn7yKF07GddfoFC70nK4nmZdACOBxOjBpY+aNbN24qtOaLHSCrCeRk2e7sjYA\nQ2Hr1o1Y1M8MMZtiyTpX+lt0rtTCPpclg5+LqVYFBj+TgsFNU6BaHWvEvmHr6qVXAq0jeva59Gvm\nBJwtuNBz1hR/+vBhPHJoztY1k7kiQoIbY70+2yJ6USqjWKogJLjRG/DA6SC2RPSpvFQz8m8oJCCZ\nl0y3KQCq0Xc7MWJCbzTFUu1Fb0DgBnrMDTeRs270InqWiWT05NF+XqwWNpjFSOaNmrpq4HMYDAtY\nyhQa2lkzoW/WnsJuuNBz1hTff+40HnjhtK1rJvNylLyp14/TNkX0rP1B2OeG00HQF/BYavlbTzIv\nIaIR+k6yNoxk3QyFBDgdxHBEz6ZLBbzGNmMBY0JfrlAUy/oePYt+l01cJQDGBLlPp98NpRQ/2j+F\nRK5oat2hkABKGz8H9j0bAnM24ULPWTPki2XkpTIOnEnYMlmJoQp9XwBnlrOWe8doSdUN3B4Iei3N\nX60nkS/WzHYd6mAcXd5AZojL6cBQSDDcxTKnWDd6vW4AmLK0jFosEb8HhBjz0QGtl24svRJobd0c\nnc/gsz98BT87MKPZ/9CX0KGwvG69fcMavvGInvOmIq6kJ6YLJZxcsi8TIZGXEPG7saHXj2yxbFgk\n2pFUOleGlEHWsaC5nPHW60q1Qh+yXnAjShV4XA44HK37xgPmsmNyOmMEtTArxMjaRi0Wp4Mg6jdR\n2GSwghXQ73fzylQCgGy5GE2vBDRXZXUn68V0AUGvy9BJqFO40HPWDCuaP7CXziRsWzep+N6beuUm\nXnbYN8y60Ub0tlg3uVqhHwx3Zt0Y2SyUc9PbH3ulQlGpUGQLxoXe65JzyI0JvblN03Y9aSil+Mh3\nX8BPX54yXMEK6Pe7OagKfVE+iTr1T6JA7cl676k4XpyQs2wWMwX0n4NoHuBCz1lDaCPtA5P2CX1K\ntW5kobcj8yZVl+8eCwpYyhRR7sByKleUFsX+ahZGUGnkZaVXSr6o38wLkCNOvTYLf/SDA/j9B19C\nXlKybgxYNwBrWaB/kjIt9G0i+uVsEU8dW8ILJ+O6c2ibrd2q383BqSQA2cMXpXLLkYfN1pQzvvL4\nvQdewp/8/HUAwFK6oG5Yn2240HPWDOyPdzTiw8s2RfSUUiRy8gZnJ353PapHL1Qj+nKFYqWD6lgW\n+fb6qxE9IQRDYcHyUGyjEX0iJ7UtFHrhZBx7T8XViF5v8AjD6JVO3oTF0qfTfIw1mJMjb9bYzZjU\ntep3UyiVcXhWHhkZzxZRMPjZAvLvcDDsxY9fmsJSpoBTS/I+0VKmgP7g2U+tBLjQc9YQTOhvvDCG\nI3MpdeOvE3LFMkpKf3erbW6bkRIVj94nR7YDFnula3nhlNwvf/emaM39gxY6NgIsojcg9KH22THJ\nvIS5lIilTFEddG2k1w2g3+/mMz94Bd967JipTVO9iJ61jF7OFkxdKQDyhmyzzdg3ZtOQyhSC24Fl\nxbox462zNFlAnjmwlCliMV04Jxk3ABd6zhpiJVeEgwDXnT+ACq1eKneCtqUAa3Nri9DnJQhuB7wu\nVsHaedHU8yeXERRc2DESrrl/KCRYGuJtpNIU0OSmtzhJHdd05mS/E6PWDYvom2U6pUQJ/3xgGk8f\nX0LBZBpkIi+1tMmqEX1BnQRlxrppdhJh/vxbz+vHcrZoqMe9FrYh+/Zt/QCAY/NppMQSt244bz7i\n2SKifg8u3RgBABya7lzo1UEeih0yGLRmg9STVPrcMMzkjLfiuRPLeMvmPjjrNvhGIj7Mp0TT/n9B\nqhizbtSIvvnnoh2qcnAqCa/L0XCMrRgIeiFKlaYTlp4/sazMlS2Y2jTtDXhAKVo2kTumDFRfVqwb\nBwHcTmPHy/rd1BeoHZxKojfgwc7RMJJ5CZlCyVREv6U/gB6vC5/7tQsAAC9OrAAA34zlvPmIZ4uI\nBjzoC3jgdhLb0hUBTXZMyJ7sGNa5kqFaNxZz6WcSeUws53D1eX0Nj41GfShVqOkTVF4y1k5Yb+j2\n0fkMBLcDgtuBZF4ylHHDaHcCfOrYEoBqFgtgzKPvbdOThlKKY0pEn1NSaQW3U20pocc2ZeziR+9/\nsWYv5+BUEpeMhdWCrZlE3vBVAgD83g1b8einr8VFwyG4HAT7TsuZNzyi56xpHnplBv/roL2j+eLZ\nInr9HhBC5N7gBlvGtqO+G6RdEX0qX1Jz6AHZyujxuiyfRJ47IfvzV29pIvQRa0O88wbthT6lhUOr\nz+XYQhrbYkFs6Ze7dBq1bQBgoEc+iRxfyNRYQADw9HFZ6JN5CWklXdVY3/jWrQqWMkWs5CRsHw4B\nkGcQmIm8f23HIL5++068dDqBu//+RQByNtSJxQy2D4fQqxRVzSREw1k3gHylMhz2weV0YEOvHy+d\n5hE9Zx3wnSdO4KsPvd5ROmE9K7mi2lhKTnOzQ+jlNdQ0yJAXuWK542HN9YVNAKuOtSj0J5cR9btx\n4VDjIO9qPxpzQm90hJ7DQdDf42m5kXx0Po1tgz3YGmNCbz6iv+f7+/GOP38SE0tym+jJeA6nlrLY\npqw5rbRgMNN8rFlEz6L5q7b0ApCbtRnpc8MghOADV27EXdeM48h8GuUKxXKmgFKFYjgsqEVgRvc/\nmjHeJxfuAeemoRlgUOgJIX9ECHmNEHKIEPIgIUQghGwmhLxACDlOCPknQohHea5X+f648vj42fwB\nOKtDIlfEUqagFn/YQTwrIar8EZsZ69aO6sQmeV21zW2HUX29dQN0VjS191Qcb9nc17QAZyRibYi3\n0YIpQLZvmp2kknkJ86kCtsWCVaE30LmScd5AAHddM47funwMQHVuL4vm37d7FAAwqQq9sSsQoHlE\nz/x5dmU0vWIuomcMR3woVygW0wW1KnkwJKjvLR+rRaHvD6i310zWDSFkFMCnAOyhlF4MwAngAwD+\nDMA3KaVbAawA+Kjyko8CWFHu/6byPE6XwdoV/OLgrC3rVZQc9N6ALJ59AY/hxlXtSOYlOB1Ezfuu\nNgmzvjalFPFssWlEv2RR6JcyBWzo9TV9zO9xIep3G4ro956K43vPTQCAqRTAwRbZSMxuOV8b0ZsQ\nOJfTga+8ewd+/8atAKr7AM+fXMZgyIurFEFmU66MHC8LBppZe0fn0wgJLlw0Ils3ealsqBd9PSNK\nzcVsMq/+XxkKCWrPegDwmbCdZ37MAAAgAElEQVRutGxWhD4onJv2B4Bx68YFwEcIcQHwA5gFcCOA\nHymP3w/gvcrt9yjfQ3n8JmJ0J4SzLhClsrp59i+H5myxb9JiCeUKRdTPInrj8zvbUd/fPdbhpikA\nTMbzSIslXFBns1gZXA3IJ468TvQ9GvWp9kY7Htx7Bv/tkSOG1tQyEBSaXuWwWbjnDwYtWTeMagqn\n/B6T8RzOG+hRNyOnVvIgBPAasFncTgdCgqtpl8lj8xmcPxisiZTNpEEyhlShF9WIfigsIOJzg110\nWbduZKE/F83MGLqfAKV0GsA3AJyBLPBJAPsBJCilzOicAjCq3B4FMKm8tqQ8v3GHibNuYdWf12zt\ns82+YVcIzAPtDXiQK5bVvt9WqW/7G1Mi+k4Km16elDfSLttQW9gUCwrIFEqGx9wxCqUKKAWENgI6\nGvEZiuiXs0VkCiWkC/KJ02hhUyzoxXK2iNdmkrj2vz2O08uyl35sPgOf24nRiA/jfQE4HcSUdcPw\neZwICtXN6vlUAUNhQRW7uZQIr8thODum1R7OdCKPjb1+CG4nepTjNJMdwxgJy1dXs0kR80kRTgdB\nf48XDqWpGmBd6FlEf65sG8CYdROFHKVvBjACIADg1zt9Y0LIPYSQfYSQfYuLi50uxzmHrGRl3/v2\ny8bgcTnw2OH5jtdk0Rn7I1L7jrcpdTdCsm6QR0hwwetydBTRv3wmAZ/bifOVWbGMYc3lvhlEAwNC\nRiN+zCTyui2W2ec4FZePwUiEDFRz6b/+L2/gTDyHV5TCqKmVHDb0+uBwEHhcDtx88RD21FXuGoX1\nvakoqaJDIUEVZErNCWezwiZKKRbSonoyZ0GDlYg+4nfD63JgLpnHXErEQI9XrR1Q1zWxyatlJOKD\nx+k4Z6mVgDHr5h0ATlFKFymlEoCfALgGQESxcgBgDMC0cnsawAYAUB4PA1iuX5RSei+ldA+ldM/A\nwECHPwbnXMIKVUYiPgyFhI78bkZcOXlUs27a9wY3ymK6oK4JwJbq2JcnE7hkLAyXs/bPZ0RNgzSf\n7w7oD/HOFctqAVgrmG89qXjeRiP6QcVaYbntLJVzNiliOFzdO/ir/7gbd7513NCa9cSCAhZSBSxl\nq1ksQPWkbiby7g00WnsrOQlSmWJQOWmxjVMrkTchBCMRH2aSIuZTotpFVH5veV0r3j8gt1q+623j\n+I1Lhi293gpGhP4MgKsIIX7Fa78JwOsAHgfwm8pz7gTwM+X2Q8r3UB7/FbVj0gNnzbCiiE004EbY\n564Zvm15TeWPturRt06hM4oolXFsIYPtw7Veeie59IVSGYdnUmr1rharET2zp9qJMsulb2ffUEpV\nO4N16DScdROqRpcuB9EIfR4jEaHVy0wRC3kxnxbVQiS2Mc4sDDORd59i3VBK1Ssi9jtl+wF96rrW\nBHlIGeo9lxQxpPl82ICSTjZSv3jzdtyycw0JPaX0Bcibqi8BeFV5zb0APg/g04SQ45A9+O8qL/ku\ngD7l/k8D+MJZOG7OKsI8+qjfY5vQ13v0LBozOli6Ga/NpFCuUOwcrRXlTiL612ZSKJYruGxDo9AP\nhQUQYj2ibyccRoZ454pltbfLlIm8dKAqjldu7sXWWA9mEnkUSmUsZYo1EX0nDIbkiJ717WHr9lsQ\n5N4eD1ayRfz3x47hLX/6GESprP5OWURv5QSiZTiiCH1KVE9KQGeW0GphaFeFUvoVAF+pu/skgCub\nPFcE8FudHxpnrcKi74jfjbDfbbpisxnxbBFel0ONQPvalLkbhTWi2rWhtklYLCjgyaNLltY8oLRP\nvmxjo0/tVnxXs5+HMY9eP6LXflamI/qgF7ftGsGHr9qEv/33E5hOVCPv4bBNEX3Qi0KpohY1DSoj\n9lirXjNWSF/Ag1KF4lu/Oo5yhWJqJdcQ0TNLyGthMxaQf+7ZZB4VihqhZ9aN0c92LbB+TkmcNcNK\nTkLA44TX5bQvos/KVbEs6yLgccLjcnRUNPXqVBIDQa864YcRC3mRKZQstUF+eTKB4bBQ84evZSTi\nM91pMl+Uo/B21k3E74bP7WybYqn9rFhEb7RM3+Eg+MsPXoYrN/fK3nQir16ZsL2HTmEZNq9MJeFy\nEPQHaiNvM3npTGwriis8sZRTM3piNnj0gHzFwTKHh2oi+s6tm3MNF/ouJy1KtvR20ZLIFdVKUyb0\nnW7DNNs07Q94OtqMfWUqgV1j4YaUPbbxaCXFcmIpi/MHG9sUMEYiAmbMevQGInpCiJxLn2g9HUub\nV65uxlopFor4kMxLOL4o59DbFdGzk+PBqQQGQ4JaBcxOAGb7uwPAJ2+QC7EmlrOYT4kIaYqQ+jq1\nbjQ/95DmdvUEsn7kc/0cKccSf/bIG/jgvc/buuZKroioUsEa9rlRqlB1aLRV3phL4YI6Ae3taT9J\nqB1pUcLJpSwuGWv00lnEZ+UEmCuW1PzsZgyHfZhNiKZOfEY8ekAW4Lk2Jyd2UhwJC+rvw2jWTe37\nyKLGGm/Z5dGzYjWWQ89QvXQTFstVW/rw/Y9eiT98x/kICS6cXpatm+ZeuvWInqFdl42kZBbReoAL\nfZczvZLHyaWs2h3QDlZykpodw9oAdGLfLKRFzKcK2DFa66X3NUmhM8qr00lQClwyFm54TK8tbztE\nqdJWPIfDAvJS2dTnIRoU5cGgt22PHvZZna+p2LVSLMT2A/adjiPqd1s6WTQjFmoeIVvZNHU4CN6+\nbQAOB8F4fwATy1kspAs1gjygWkLWPfpmx7tjJIxnv3AjLh5t/L+1VuFC3+UkFME5vpDReaaJNXNF\nW4X+tWl5FufOBqG3bt2wSUjNIvrBDiP6TjdN6zFi3QDVbKFKi5YT8WwRHqdDLbEHrEb08s8wGc/b\nFs0DQI/XpbZPGGoiyFYj7019AZyJ57CQKqhXDQAwFvVj14aIZUGO+N0Q3A70eF0NV3F27VucK7jQ\ndzlJJeddOyWoU+RJUFXrBuhM6F+dToIQqI2oGH2KdWPF/z8yl8ZIWKjx/Rlhnxsel8NSp8lcsdy2\n18uwIgCzJlIsjQr9YEiQ2+a2uMpZVja0ta1vrYhnLFitArUrh57BIm5ttMyybjpp+zu1kq+pigXk\nk9zPPnENLrdYyUsIwXDYpwYG6xku9GuESoU2jC+zAybAbI5mp5TKFaTEUs1mrPZ9rHBoOonNyqg1\nLb0BeQydFf8/la+2PK6HEGKpAVmlQlEote8IOWKhaIr9fHrtCuobg9UTV4W+s4ZeLqdDjbjtjOiB\n6sar1mLxe1zYMhDAloFAq5e1ZWOvH+UKhVSmNRG9HewaCzdcaa5HzHcn4pwV/mnfJP6/fzuK5754\nI9xOe86/lFLVujlqk3XDBN3OiP7QdBJ7xnsb7tdWxwZMNtLKFEptXxMLei2N5gPad2/s7/HC7SSY\nMZFiyQZNN+tFr4VFlnKfnkbxWc4W0ddTFXoHATwW/y+NRARMJ/IYtjmiZ0Jcn8nzq89cb3lNbX/3\nVmmvVvmLD1xm63qrBY/o1wgnFjJYyhRq5lR2SkbpYAgAx22K6KvtD2QRZg3DUhaFfjlTwExSbBo1\ndVIdmy2WEGwr9M0HbbRDtVjaCL3DQTAYEkwVTeWLxtoJD+p03oxn5RRVFjX7TMxKrYd50CM2R/Ts\nqsROQWZZMEBtKwdOFS70awQWEbP8ZztgDbA29wcwkxRtybxJ5Gp70gS9LhBiPaI/NCNvxO4YDTU8\nxiLTJQsbstlCuW1EPxiyENEXjXnpIxGfaY/eiNAPaNITmxHPKNaNhbz0epjQ25VDz7hkLIzBkNdW\noR/o8apXWYPrKOXxXMKFfo2QUkSYtZe1Aya+V4zLm1HHbLBv1IheEXqHgyAkWK+OPTQtZ8fsGGmS\nBlljVZhD17oJCUiLJVP7IlXrpr2NNBI2VzSVl8pte9Ez3E4H+gIezDf5PESpjGyxjL6Ap+OKUEDe\n4ASAjZpo2Q7ee9koXvg/3gGPxRa/zSCEYJOSacQj+uZwoV8jnM2Invnfx2ywb1hDs4i/2uO9kzYI\ns8k8on53w1g+oBrRW6lgzYgl9HhbC111dqzxtatFSO3/bIYjPsynxJZpkPWIBq0bQD5BNculZ7+X\n3oAXglse8tFJ/vv7LhvDj//z1bZvxp4txvv8NVWxnFr4ZuwaIZWX+66YHQDdjkRe/uPfORqG1+Ww\nJcVSbSesyWjpROjzxUrLCNntdKA34MGiSY++XJHH6OlF9AAwnxYNR61V60Y/opfKFEuZQk26X8t1\nTYz8ky2nxs+D1RuwdNKBHm9HJfoelwOXb2rcIF+r/Ke3b8YNF8RW+zDWLFzo1whqRB+3P6LvC3hw\n3kCP2rukE1ZyEjxOhzpsG5Cje8tCL5XaRp6xoNd0RJ9VmpW1a1VgJaLPS/K6epHyiKZoyqjQtztW\nLYNBAa8r+xpaWFUsy1Qaja6PSNwuLt/Uu65OTOcaLvRrBObR22ndMPEN+dyIhbwdT2sC5ArbsV5f\nTTZHyOc2VQmqRa8AaSDoxaJJj57NbG2/GcvaIBhfm1k3esOxhzXzRo0k5+WLZcNj5QZDXixlCihX\nqFrUBFSFnkX0X/8Pl3TcaI7TPXCPfg1QrlCkxRLcToL5VAGFkj2FU4lcEYLbAcHtRK+/ccamWSil\neOnMCi6v68Ue9rktp1fmi+W2vmosKJiuYGVC3y5Kjvrd6udtFONZN/JJpF2K5Uwij394/jQAeSPV\nqJ8+EBJQoXJaqhZWLcs2YkcjPoxF7d1I5axfuNCvATKiLEwXKM2o2vUcN0MyLyHik//wowGPumFn\nlZNLWcSzxYaS8k5aFeclAxF9xlwbhExBFuR2Qi9XxwqmInojefSA/Hn43M62fen/9t9P4Mv/fAjx\nbNGcR69JsTw6n4ZUlnvZL6REeFyOppvaHA4X+jUAs1h2DMsphpM2CX0iJ6nZMb0BD3LFckdtFvYr\nbWv3jDcKvVSmqhCaIa9j3cSCXkhlqqZ1GoGdOPWqaQdM+v9GI3p5sLTQtg3CU8flCVfzKVH3qkYL\ns5x++vI03vXNJ/GzAzMAoIy781oukOJ0N1zoTUIpVa0Bu1CFXikamrLJp0/kJTXCY3nvnUT1+ydW\nEPa5saW/p+b+Ttog5PSsGyUv2ox9k1E9ep1ukEGvJY/eSPQ9EvFhukXR1HQij5OLWQCy0Ou1PtbC\nhP7/f+YUAOCkssEuD7DmxUKc5nChN8kjh+Zw5f/zS7VC1A7YRuzWWA/cToJJm4qmkjURvfx1JWu9\nOnb/mRVcvina0JOlE6EX9aybHvNFU0Y8ekAZVm3iBGK0Jw2gzBtt4dE/fWxRvT2bFFEsVwxbN/09\nHrCg3eN0qJvg9UM3OBwtXOhNcmo5i2yxjMOz9vSOAbSNwjwYjfhsy7xJ5ItVj77DiD6RK+L4QqZp\ny1dV6E3YK4ycTrFQTKe/SzNYeqWedTMY8iKRkwzbWXrHqmU47MNipoBiqdLw2JPHltRWwqeXzY38\ncyn95t910SAu2xjB9EoelFJ5ahMXek4LuNCbhBU2HV+wT+hZxkrY58aGXr9tRVP1Hj0Ay5k3L52R\n/fndG9sIvcmInlLZ1/e1aSnA8t3NFE1lDEb0Q0oapNFGcvLGsbGM5JGIAEobWwqXKxTPHF/CdefH\nEPG7cXpZtnCMtEBg/Pg/vxXf+o+XKTNk80iJJeSlcs0UJA5HCxd6kzCb5aiNgzySGqEfDguYMzlc\nuhmiVEahVEFYEXpWyWo1oj+1JEeeFw41DsZmQp8wGdGLkhzttotmA8pUIlMRfaEEl4Po9ncfVnvH\ntxb6hZSIL/7kIESprGyaGvuTYUVT9SmWr8+kkMhJePu2fsSCXkyYjOgB+aTtdTkxprRaYFlaRoqz\nOG9OuNCbJK1kdNg1yAOQTx5OB4Hf40TU7zEtmM1gazDrJqKIsdWIntkbzeyQWMgLQtoLZjOM9HcH\nzG+aZkS5oZleBsqwgSEh/3JoDg/uncSr00nkiiXDEb22aErLMeVKUO7iKKgRvZW5pqNRHyoUeGUq\nAQDcuuG0hAu9SZjNYkcnSEZSyY4hhCDsd6NQqnQ8bYr1uWHWjcsp51ivWBT6fLEMp4PA7WwUT6/L\niYEer6ke7IA8fxUwMCvVZNFUpmCspUArMdbC2g0spAqm8t3Voqm6kwj7OQZDAmJBwXCjtObvIR8/\nS3vlQs9pRdcKfTIn4fM/OmhLD3YtbL14tmhpIEYzUvkSQoIsTHakQQLaiL5aQNMb8CBu8WqBiVyr\nKHkk4jPVmhfQ5KXrRPQDIa8poc8WSrqplex9I35324j+tVm5jfJCWs53N5oG6fe4EPa5G/rSL6QL\n8HucCHhdNbNIrXRdZIPIX1KEnrfo5bSia4V+70Qc/7RvEs+dWLZ13ZRYQlARZbsGbic1+e4Ri353\nszUBqB49IJf9W47opfb57qMRn+l+N0atm4Eec/Nds8X2vei1DIWElpuxUrmCo3Py73heiej1jlXL\nSMSHp48v4cWJuHrfYrqgbjBr0yGtWDcsoj+5lEXU7+Ytejkt6VqhZ5H3hOKB2rkuyzw5ZlPmTUqU\n1JF8EZsiepbqyNYD5KsFyx69zkbkSEQen2emVYHRAqRYyItMoaRaPXpkCiXD3SBHIj7MtChsOrGY\nQZG1GEiLptIrAeBTN25FMi/ht77zHP5x7xl1neqA7GoEbqV3vOB2qj37eQ49px1dLPSyKLBsEbtI\n5UvYFutBUHDZtiGbzGuF3npOupaD0wkIbofa5AqQM2+sFnrp+dMjER9EqWKqVYHR3jFszqjRzJus\nCaEfCguYazFSkPnzIcGFxXTBVPMxALh55zCe+fyNGAoJeP6kfGUpR/TyzzMQ7CyiB6rtiHlqJacd\nXSz0SkS/ZF9EL5UryEtlhHxunD8YtC3FMpWXEBLqWxVYF/pyheKRQ/O48cJYzeW87NF3IPRtRK5V\nOmHbNQ169GyTsZUgA/LP/PNXZlCuUDXrxggjYQHxbLHp5vdrMykIbgeuGO/FQqpgOqIH5J9tc39A\n7V+0kC40j+gtCv2Y8rnzjVhOO7pY6OWI3k7rhq0ZElzYFuvBCRsybyilSOVLVY9eieg7sW72nopj\nKVPAb+wcqbk/6vdAlCqqwJpBr/HWqGbYhpk1AcCvM7FpKMw6NrYW+sffWMAnH3wZvzw8b8q6aVc0\n9fpMChcMhTAcETCfFk179IwNvT6ciecgSmWkxZIq9OwrYK5gSgvL7uE59Jx2dK3Qs8Km2aRoSdia\nrqlscAYFNwZDApazRZTKjSXuZhClCorlCkI+WZgEtxOC22F5YhMA/OLVGQhuB264cKDmftbvxkpU\nLxqwbgD9iF4qV/Cln76Kk4sZ5JQoWtBJLWT+c7sK1hdPyxueByYTyBbLhrJuADmiBxrTICmleH02\nhR0jIcSCAhI5CZRaE+SNvX4spgvq9DAm8F6XU61Ytmzd8IieY4AuFvrqxt3puD1RvRrR+9yI+q03\n8tLCTkjaPuIRn8dydoxs28zhpgsHG4p7VFvIwtp6Hr2c9eHQFfojc2k88MIZPHZ4AaI6sal99B0U\n3Ah4nG2tG5Zi+OKpOMoVajzrJtz8JDKTFJHMS9g+HFKzZADAb0GQN/TKA0BYGwltJB8LeuFyELid\n1v4UR5XhIuyqh8NpRtcKfVoswaOUwNvl0zNRDgouTUsBe9IgmUcPyPZNwuIJ5MDkCpYyRdy8c6jh\nsU763eh59HIP9tYZLIwjc/IG9mKmYKrt71C4dRpksVTBK1NyvvuBSblK1Kh106po6rTyf2brQE9N\nfrrRylgtTOhZYZP2xDEYEixH8wDw9m39+IObtuGt5/VbXoPT/egKPSHkAkLIAc2/FCHkDwkhvYSQ\nRwkhx5SvUeX5hBDyl4SQ44SQg4SQ3Wf/x2gkLcrRGGBf5g3b4A0JbjU67rRdsbahGSPid1telxUW\nnTfQ0/BYJ/1uRKmim6dtJJf+iJKptJCSPW+Py1Ez+7QV7bJjDs0kUSxVcP0FAyhV5PROo0LPiqZm\nEnn8+b8dwUOvyIM8WGO5sahPzZIBrFs3ALDvdGNEv7HXr/5erCC4nfijd57Pc+g5bdEVekrpEUrp\npZTSSwFcDiAH4KcAvgDgMUrpNgCPKd8DwM0Atin/7gHw7bNx4HqkxRJGwgL6ezz2RfRK58qg4FKF\nvtM5rCyS7A3U5rtbLZjKFloXIfV2cMyigYyTkbDPkHUDyBF9vlgyvLk5GBIw3yKiZ7bNXddsVu8z\nat0AclT/05en8Ze/Oq7OcZ1aycFB5BNMTURvQVD7Ah743E6cXMzCQYC+QHW9z7zrfHzv7itNr8nh\nmMGsdXMTgBOU0tMA3gPgfuX++wG8V7n9HgDfozLPA4gQQoZtOVoTpEUJQcGF8b4ATtmUecOsm5DP\nrWbHdFrB+sKpZQQ8TnVeLCAXOVm1hHJSa9877HPDQTrw6HU2TUciPiyk2w83V4U+ba53zJAyJKRS\naSzI2jexgg29PlxzXp9a1GU0ogfk5ma5YhluJ8EpJSiYSuQxFBLgdjrQF/CCXXRYKWwihKhRfV+P\nt+YKJuL3YLw/YHpNDscMZoX+AwAeVG4PUkpnldtzAAaV26MAJjWvmVLuO6ekxRJCghvj/QEbPfoS\nCAGCXlfHbX8Zz51YxhWbe2s24yJ+N5L5oqVh2zmlF3uzSNnhIIj6PVgyKfRSuYJShepH9Eqq33yy\neWFTMidhLiXCQWShz5noHTMUFlCqUCxla9emlMqTrzZG4XI6sGNEnrtrJqK/8cIY3rF9EJ+4YSsW\n0wWkRQlTK3mMKRudTgdRK1CtCD1Q9enZxCwO51xiWOgJIR4AtwH4Yf1jVFYkU6pECLmHELKPELJv\ncXFR/wUmkMoV5IplBAU3NvcHsJAu2DLnNZWX0ONxweEgCHiccDtJR5ux8ykRJxazuHpLX839Ub88\nbDtrIS1Ub4Ozv8eLJRN9Y4BqBasRjx5onUt/VGkZsWtDBCs5Ccm8ZDiiZymW9SeRhXQBi+kCLt0Q\nASC3/wWAHoPplQDw4as24e/u3KPu6Uws5TC9kseYUnWqfX+rG6cbeuW1eOMxzmpgJqK/GcBLlNJ5\n5ft5ZskoXxeU+6cBbNC8bky5rwZK6b2U0j2U0j0DAwP1D3dERqx66ewP1I5Ok2mxpLYqIIQg4rfe\nUgCAWhZfnzHBeshbWTtXLMHndraca9of9GDZZEQvGqxg1culf0Oxbd6+Vf55p1byhj36VtWx7KSy\nsU+OmN+xfRCxoNdS75ctioVydD6NuZSothcAqpkyVgqmgOqGLI/oOauBGaH/IKq2DQA8BOBO5fad\nAH6muf8OJfvmKgBJjcVzTkhrhN6ubpCA7NGzzpWAvLnZyWbscyeWERJcuGgkVHN/J/5/TqdYqC/g\nNX3SU3vS6ESzLCe9ldAfnUsjKLiwc0yOvqdWcm3HCDZbu17oWcrlUEgW5Wu29mPvl96BoCZd1Sgb\n+/xwEODZE8soV2hNRM8iccvWjWIDaTNuOJxzhaG/MkJIAMA7AXxMc/fXAfyAEPJRAKcBvF+5/2EA\ntwA4DjlD5y7bjtYg1Xx3N6KBzlsKMNKi1Jjv3sEJ5NkTy7hyc19DemFETd20OGy7jRh1Yt3oCT3r\nptiqL/2R+TQuGAyq0bFUpvAZHM3Xr2xi1mfesJMK2x/oBK/LibGoH08dk63E0YhffYylWFq1btgV\nR4wLPWcVMCT0lNIsgL66+5YhZ+HUP5cC+IQtR2cRbU+aTkSznlS+pI6fA+Q0yBOL1vrdzKdEnInn\ncMfVmxoei3bQ7yZXLCHQJkruD3qQLZZNDdFgLSSM5JCPRgRMtyiaOjafxq9fPFwT1RotQHI6CAZ6\nvJhLiXjk0CzcTgdu2j6IuaQIwe2oqUPohM39Afz7UVnotRH9b+0ZQ1+Px9KVAgBsi/XgizdfiN+4\nZET/yRyOzXRlZWxaG9Hb1N8dqO0bDwDRgNvyZiwrbGLZGFrYsBAr1bG6Eb2Sw23GvjEa0QOyTz+9\n0ligJkplrOQkjEV96Oup1gyYKfQZDAvYeyqOTz74Mv7ffz0CAJhNiRgO+3Tnwxpls+LTEwIMa64S\nxqJ+3HH1uOV1CSH42HXnceuGsyp0qdBXPXp5FmvnrQrYulqPnm3GWkmD1B5jPepmrAX/P1cs60b0\ngDmhFw1m3QDVQR71nwnrJR8LeuF1OdV9CDObm0MhL87Ec5DKFCeXsihXKOaSoq0NvbYMBGqOk8Pp\nBrpS6LWFTU4HQUiw3lKAQSlt8Oh7/R6UKhRpC6mb2nYK9XhcDgQ8TksRfbZQ0vXoAWA5Y/zzyBfl\nDp1GI/q8VG6wyhbSsp3D2umy7BMznjfL6rlt1wiKpQom4znMJvI1kXenbOmXW0ewHHoOpxvoSqGv\nj5ajHW6aAkC2WEaFQm0nDGiyY7Lm124X0ctreyx69GUE2gh9X49x6+b4QgZpUTJl3YwqolufS89m\nvsbqerGbyWK559otuO+uK3DnW8cByJu78+lCzb5Jp2xWInpWE8DhdANdKvQSBLdDrTa1KppatL3o\nGZ34/yyib1Wq3x/0qj6+GWSPvrV1w0YL6gk9pRTv+5tn8O0nTlQLpnRaIACtc+kXlLRIJvRW8tKH\nwz7ccEEMW2Ny1P2ckgbJhofYwXBIQCzobUh55XDWM+Z7rq4DZC+9Ng3SjFXRjFQTq6WT1M1MgUX0\nzbM4hkMCjlvI6JGzblqLp+B2Iuh1YUnn80jkJKTFEs7Ec+oJzah1AzQR+nQBLqUFA6CJ6C2kK4Z9\nbsSCXjx9fEl+TxsjeoeD4LHPXNdR62AOZ63RpRF97aZp1IaI/syynEmiHfDQSepmWizB63KoPfPr\nGY607r/eikqFyuPudPq89Af1i6ZYYdK80k4YMLYZ2xfwwONyYKbu2NmsVFaxa8W60bI11oPjyihH\nuwdjBwU3XBYHgXA4a5Gu/N8sV7DaV9gEyAMtXA6iNs0C0FGr4lTdVUc9w2EBmUJJvZIwglgqg1J9\nO6S/x6Mr9POq0MtdJgO2vicAABlSSURBVN1OY1OQCCFqX3pKqZp9s5Au1BQLdRLRA1DtG6A6PITD\n4TSnK4Ve7lxZG9FnCiUUS9bnu758JoHtw6GaqJalblrJ6JEzeFpH3sNthla3gjU0a2fdAHIbBD0r\na14b0esMBq9nJCJgJpHHF3/yKt77N88CkD36Ac0AD9ayIGSx0GmbIvRel0MtMONwOM3pSqGv70kT\nVQuQrM9hPTiVwGUbIzX3Ox0EYZ+1oql6e6meYZ2+Mc3IFVjzMT3rxkhELz9eKFUwlxTNpUGGfXh1\nKol/fHESr0wmkMgVsZgu1HRufMvmXvzNh3bjyvFew+tqOU8R+uGwYFuxFIfTrXSl0KfFEoJerXUj\nWyxJi/bNsYU0ssWy2gpXi1X/P11nL9XTamh1O3KSvMGrF9H393ixkpMglVtf4cxrmodNLGdNeekj\nER9KFapOzTowmcBytlhj3TgcBLfsHG7ZZVMPZt3Y7c9zON1Ilwq9VJPvXk2DtCb0L5+RB05ftjHa\n8FjU77Yo9KW2U5AGQwIIQcOmZvO1JOSLZXWMoJ4os1z6dpOm5lMiWKB8Jp4zFdFftjGCoZCA7965\nBwDwxBG5d4x29mqnDPR4EfW7eWETh2OArkuvlMoViFKlYTMWsN7v5sCZBCJ+N8b7GkWlv8eLCQuj\nCjOF9taN2+mQm3i16ASp5XfuexHjfQG87zJ5kJfedKUBpdfMYqagVqrWM58qYEt/ACcWs8iZ9Oiv\nvyCG5754IwghGIv68Ks35FEFdnZuJITg7+68AoN8kAeHo0vXRfSzicZh29X+7taE/uXJFVy6IdLU\nCx4MCaqfbYb6XP9mDEd86vDwVhRKZbwymcAbcylki7J1oxd996vVsa0/j7mUiEvGqlaV2ewY9lld\nPBLGmbicmmr3dKXLN0V5RM/hGKDrhP6ZE3IRzVVbqpt8nVg3+WIZxxYy2DXW6M8DwGDIi2ReUht/\nGaFcoboRPSAXTekJ/YmFLEoViulEXm0nrBfRq20QWlTelsoVLGUK2NDrVzeyrea77xyrpqPaad1w\nOBzjdJ3QP31sCUMhAecNVPOs/R4nPE6HJesmJUqgtHU0yqwPM+0KqlWxOkIfETCr5KO34o25FAC5\naIsdg14efauxfIzFTAGUyiexTmel7lBaCRAi5+9zOJxzT1cJfblC8fTxJbxtW3+NzSLPd3Vbyrph\nQ8Vbtf5Vh1a3EM1mtOtcqWU4LCBbLLftjnl4NqXeZpWiekLv8zgR9btbpm4yK2owKKg/nxmPXsvF\no3JE3xfw8GpTDmeV6Kq/vEPTSSTzEt6+rb/hMatpkKwIqZV4ss1AMz69XudKBmvWNdtiYhMgD9x2\nO+WT2tGFtHKs+nvsw+HW/r86hzUsqD+fz0BDs2b093gxHBZqiqU4HM65pauEns36vGZro9BH/NYK\nm6pC3yKiD5qP6Jl106Mj9KxZ12ybzJvDs2lctUWe8nh8PgOvy9Ewg7bp2kr1ajOqveOr1o3QwRCO\nD1+1Ce/eNWz59RwOpzO6TOiXsGMkpGaVaIkq06DMwjJZ/N7mQhfxu+FxOjCfNm/d6GXdDKlC38JL\nTxewlCng2m0DcDkI0oWS4ba/8iSoVtaNCKeDoD+g8egtbsYCwCdu2Irfu36r5ddzOJzO6CqhPzyb\nwu4mRU0AEA14LLUqzutYN4QQDAS9WDwL1g0rmpptIchsI3bHSEidsmR02PZw2IeUWFL3ILTMJeUG\nZA4H6dij53A4q0/XCD2lcspiuEWTrKGQgOVsEYWS8TRIQH8zFpB9ejMRfcqg0LudDgyFBEy38Ojf\nmJU9+QuGgupEJOMRfWtbaCEtqtlEQx1m3XA4nNWna4RelCqo0NY55KyP/ILJ4iZ1jF4bATVbNGU0\n6waQR9pNreSaPnZ4LoVY0Iu+Hi9GI3LhkF4vegbrjtnsJDKbFDGkbMJu7PUj4HFiQy8vTOJw1itd\nI/TqBmcLL91KGiQAtX9M+4heMJleWYLbSeBtMXREy1jUh6mV5tbN1Eoem5S2DKNRJaI3GHmrEX2d\nLUQpxdRKTq04Dfvd2Pfld+Id22OG1uVwOGuPrhH6nLJp2jqib18k1Ip8sQRCAMHd+qOKhbxIiyXV\nz2/FZDyH08tZtXOlkfa6Y1E/5lIiSk06Tc6nRPUExoZyB1qc6Opp1TRtKVOEKFWwIVod5uHzOHkr\nYA5nHdM1Qs8i+labkWo1qMnxfNliGX53e6Fjpf0LOj795370Cj783ReQzLfvXKllLOpDuUIbTlCU\nUswlRfXnUq0bg5uxbqcDsaC3IaKfVGwibtVwON1D1wg9s1haCWjY54bX5TBt3eSKZd1BHkaLpo4v\nZDAZz+PxNxZ0N2IZzJKpt29S+RIKpYp6paJaNybSIIfDPszUbcay9+HNwjic7qF7hF61blqnQQ6H\nBcyZ3IzNFUu6dogR/z8tSmq3SCMNzRhMcKfrhJ5F+Oy92UQqM/nuoxEfZhMi0qKEfRNxALK9JL8v\nn8PK4XQL3SP0hfYePaBsmpq0bnLFsq4dYqQ6dmJJFtDdyjhCvWIpBts0rY/omdCziF5wO/G5X7sA\nt+0aMbQuIJ8cZpJ5fPLBl/H+v30Oy5kCplby6At4dDtgcjic9cObSuiHwoLpzdhcUb/aNORzwety\ntO1geUoZTvJffv1C+NxO9PqNdXL0upwYDHkbUizZCWtIMzjkEzdsbToFqxXDER9EqYInjiyiQoH9\np1eUjBsezXM43UTXhG2qR98m+h4KyUJPKTWcRZIrlnU3TpktNN1mkPfEkiz0u8YiePCeqzBgYtrS\naMTXsDY7YZlZpx7WS+dtW/vxwqll7D+zgsl4DjtGwzqv5HA464mui+hb9aQBZOumWKqYam6WK5QN\nbXCORf0t890BWeiHwwJ8Hicu3RBRK1mNoF2bDfSeT4mI+t0dtSa4+rw+fPDKDfjz396Fi0fD2Dex\ngulEnkf0HE6X0TVCnymW4HE54G7T81zNpTfh0+ekUttiKcaG3tYVrIBs3Yz3BQy/r5axqNyA7KFX\nZnDxV/4Vk/FcTQ69VSJ+D/7r7ZcgFhSwZ1MUL51ZgVSm2MAzbjicrqJrhD5X0LdYrFTH5gplQ5ks\nY1E/ljJFtXCrnomlLMb7rQq9H6UKxZd+8ioKpQqeOLqIuZSonrjs4PJNUbBBVjyHnsPpLgwJPSEk\nQgj5ESHkDULIYULI1YSQXkLIo4SQY8rXqPJcQgj5S0LIcULIQULI7rP7I8hkDbTotVIdmyuWDWWg\nMLujPg0SAJI5CSs5CZv7rQkoWztTLCHodeH5E8uYSxZqNmI7Zfem6iYut244nO7CaET/3wE8Qim9\nEMAuAIcBfAHAY5TSbQAeU74HgJsBbFP+3QPg27YecQsyBf1q01jQC0KMWzflCkVeKhvq3Mii4Mkm\n9g3LuLFq3bB+Nh+4YiPeuWMQz55YwnK20LF1oyUWFLBR+RnM7B9wOJy1j67QE0LCAK4F8F0AoJQW\nKaUJAO8BcL/ytPsBvFe5/R4A36MyzwOIEELO+nihbLGkG3m7nQ70Bby61s1iuoDDsym1c6WR/jEs\nCp6MN0b0LONms0XrZlNfAPf9zhX441svwtVb+rCSkweW22ndAMBbz+vDpj4/7z3P4XQZRtIrNwNY\nBHAfIWQXgP0A/gDAIKV0VnnOHIBB5fYogEnN66eU+2Y194EQcg/kiB8bN260evwq2UIZoRa96LUM\nhb0tJzYx/vThw3jq2BIe/oO3AYBuCwQAGOjxQnA7mm7InlrKgpDOvO8bLpS7R159Xp96H2u9YBdf\nvvUiZMTWg8g5HM76xIh14wKwG8C3KaWXAciiatMAACilFAA188aU0nsppXsopXsGBgbMvLQp2UIJ\nAQObphui/qb2iua48NyJZSxlCmrveiPrEkIwFvU3jejPxHMYCftsiZTHon7VYrHTugHkPkF2XyVw\nOJzVx4jQTwGYopS+oHz/I8jCP88sGeXrgvL4NIANmtePKfedVbIFfesGkG2QyXgO5Urz89JkPK9u\n1h6dlyc4GW0UNhb1NT2JTMbtrTa9WhkGbudmLIfD6V50hZ5SOgdgkhBygXLXTQBeB/AQgDuV++4E\n8DPl9kMA7lCyb64CkNRYPGeNrIEKVgAY7/NDKtOWg7FfOLWs3j6iCr2xAuINLYqmplbytqYs3vW2\ncXzyxq3oDRhro8DhcN7cGG2B8EkADxBCPABOArgL8kniB4SQjwI4DeD9ynMfBnALgOMAcspzzwr/\n+tocfrhvCvd+5HJD6ZWAHNEDsp3STHxfnIhDcDsgShUcmZOF3ugwj7GoD8m8hJQoqWMCC6Uy5tOi\nrRH9hUMhXDgUsm09DofT3RgSekrpAQB7mjx0U5PnUgCf6PC4DLGUKeCXh+dxOp5DqUINWTfjSi77\nxHIW12ztb3h876k43rZ1AM+eWMJRReh9boMRvXLiePjgLJ44soj/+30XIy2WQCl4tSmHw1k11nVT\ns/MGegAAB6cSAFoPHdEyGBTgcTlwernRS59PiZhYzuFDb9mEyXhOtW6MRvRMzL/wk1cBADdtj6kb\nprwIicPhrBbrugXClgHZhjk4lQRgbNPU4SDY1OtXc9u17D0lD9+4cnMvNvTWzkw1woZeHwiRj8vv\nceLQdJKP5uNwOKvOuo7oB3q8CAouvKoIvdE5rJv6Ak0j+tdnU3A5CC4aCdWM0jPS1AyQm4R9/+63\nYMdICB/7/n4cmknB73XB7SS2p0JyOByOUdZ1RE8IwXkDPTg0Iwu90alI431+nI5nUa5QPPDCacSz\n8oi/iaUsNvb64XY6aqwWIy0QGG/b1o9owIMdoyG8PpPC6eUsRiI+OB3G+t9zOByO3axroQdkmyRX\nNN6qAAA29QcgShX8w/On8aWfHsIP98mFvKc0HSaZ1eJzO+GwINI7R8PIS2U8c3yZ+/McDmdVWfdC\nzzZkAXMRPQB849+OAADemEuDUorTyzm18RgTZ6PFUvVcrExpSuYlnnHD4XBWle4SeoNe+qZeWczT\nYglelwOHZ1OYTxWQl8pqK2EW0bebWKV3XIJb/nh5RM/hcFaTLhD6akdIoxH9SESAy0HQ3+PBh96y\nCScWMzi2IKdSMusmJLgR9rnhN5hDX4/TQXDRsFzUxDNuOBzOarLuhX5jn1/d6DTq0bucDnzk6k34\n8m9chF0bwpDKFI8dllv1aHvGj0V9hlMrm7FTsW94RM/hcFaTdZ1eCQBelxMbe/2YWsnB6zIuyl95\n9w4A1cZl/3JoFh6nAyOaoRufvHGrOl7PCm/bNoAfvzRdYy9xOBzOuWbdCz0AbOkPYCVXtPxaj9OB\n+VQBW2M9NWmQv35xZ/NS3nnRIA788TvhajOwnMPhcM42XSH0v3n5GLYOWouaXU4Htg324LWZlOVR\nf3rrczgczmrSFUJ/885h3LzTevR94VAIr82kLA/v5nA4nLUMDzcBbB8OAqhm3HA4HE43wYUewGUb\nowCAHSPhVT4SDofDsZ+usG465fJNUTz1X27g+e4cDqcr4RG9Ahd5DofTrXCh53A4nC6HCz2Hw+F0\nOVzoORwOp8vhQs/hcDhdDhd6DofD6XK40HM4HE6XQ2gn7RntOghCFgGctvjyfgBLNh7O2YYf79ll\nPR3vejpWgB/v2cbK8W6ilA7oPWlNCH0nEEL2UUr3rPZxGIUf79llPR3vejpWgB/v2eZsHi+3bjgc\nDqfL4ULP4XA4XU43CP29q30AJuHHe3ZZT8e7no4V4Md7tjlrx7vuPXoOh8PhtKcbInoOh8PhtIEL\nPYfD4XQ560boCSFE/1lrB0LIevps+VyCs8g6/L+73o6X/63psKY/IELIdkLI1QBA18FmAiFkJyHk\nMwBAKa2s9vHoQQi5mhDyPwBcsdrHYgRCyKWEkN8lhAyt9rHoQQjZQQi5Hlg3/3f539pZZLX/1tbk\nZiwhJAzgGwCuBLCI/93e+QdpVZVx/POFRVdbZAdG0ESCNIfRXCLTJBxEBPePZmRktGFTMstas8aa\n8VeiklpNpDYpOtpYsSqaONqkpGRjo5bZACkwSFaT2gxRJIJFZf4AfPzjnLe9s+2+uwvce8999/nM\nvLPvuefedz/vee9z7jnn/jiwGugysxdLFesHSSuAdqDdzJ6SNNzMdpft1RuSPgdcBNwGdAE7E3Yd\nAdwKfAT4PfAWcIeZrS5VrBdi6/JWYBawibDvPmxmz0oallql5LGWH5JkZpZCrKXaor+McBCaAnQC\nY4CJpRrVIdMd+xVwM/ANADPbnXC3cgJwpZndbmZvphYkPTgWGGVmx5nZOYT9NtVb20cBI81sMnA2\nsB24WFJLapV85BKqFWu1eEo+1jI9o9JjLZmCkXSWpC/G5O3AIgAzewloJQR7Mkg6U9IXAMxsVxzX\nbAe+D2yVdH7MeyeFMc+sb2zFHQOskTRL0s8lLZQ0L+an4nthTO4GPiFpVHQ8EThV0tS4bqm+kuZJ\n+m5MjgGmSXqPmb0K/Bj4B/CluG4KZTtP0s0x+QPSj7Vs+VoFYu1/vpJGk0KsmVmpL6CFEAyrgA5A\ndA8pNcW/XcDpZbvW8R0R824AmoEPA38EHgDGJ+Y7PC6/E3gcWAKcDpwHrAemJOZb2we+BdwLbAUW\nAF8HfgocVaLr0cCPgHWEg9F74/JlhBYcQBNwKrAcOLTksu3pOy6Tl2Ks1fNNMdZ6+h4al99VdqyV\n0qLvcRQ7HHjFzE40s/ug15NBhwF/idsW7jwA352SDgQOASYRuuzjgLFmtlnS8IR8a3mLgCnAFjNb\nYWZdwEpgbpGu0K9vbV9YSBifP9PMlgE3AX8GppfhKmkGoUW5ysymEoYRPhpX+yEwXdIkM9sFvAK8\nCRQ+A30/vtN62SSJWKvnK+kAQqxNJJFY68P3Y3G1qyk51soaumnOvG8DxgPErvoiSSdLarYwJHIU\n8JqZrYtDD1dLak3M9xRgP0Iw/5bQKp0FTJDUZsWPydXzvVLSKWa2CfgecFZm3bHAbwqz7Kae71WS\nZsWD/3+A+QBmtp1QKb1QsOsB8e8LwGlmtkTSfsAHgNoY/HpgLXB9dN0IvI9wErlo+vWNJ4l3STqC\n8mNtIOVbaxisofxYq+e7EyDG2p3AvMx2hcZaoRW9pDmSHgeul9QRF68FtkhaSjhi7wCuAD4d88cD\nJ0h6ktDtWW5m/0zM91LgHOARoM3MOs1sLaHVXIjrIH2vkHS+mS0CXpT0bUmrgNHA7xL0vVzSBcCT\nwGxJN0p6mhBIL5fgOt/MtpnZ67FB8jbwPKF1Sdw/rwMOk3SLpI2E+RZ2FDUmO0jfWgX6fuD4BGKt\nri/wNmEo7LhEYq0/X8zsMmCTpMVlxFqR41dHEi7dmgtMJYy3XkwYw/wO8CzdY90LCK3NJuCTwGvA\n7KJc98D3XOAW4KCYHgYMS9h3AaGbOQwYCUwmtEZS9f0U4dK0JkIXuBM4o0TXe4CFMa/meHJcfnBm\nu4MJ3fdCx7z3wrcjkVir5zu2x7YpxFrd8qX7nGNLGbFmZvlW9NkfgXB0uy2T91nCEbgVmAE8AZwd\n89qAh0r4AffG9yfum5vvlKL3h35cPxNdx2aWzSb06JqKLNN96evl2zi+/+efY8GcB/wN+GZMtxFa\nC5NiupNwdvqOmJ4LPAdcThjvuiQWrgr6Id3XfQfj+hxwd4/t/g7MKKI83dd9B/UdciqYFkIL7MuE\nMdfJcflNwH3AM4RuzbGEs8+HxPzjY6FNK/iHdF/33RPXRzOuI4DPAxMTLlv3bXDfPr9HjgU0If5d\nDNwf3w8nnIQ4KaYPJ5yNbi69INzXfQfv2gXsX6Gydd8h4NvbK7erbixcUgThyDdJUruFS592mNmv\nY94FwH+JlyGVifvmS5V8B+H6BrCrDMcs7psvVfPtlYKOiJ3ALzPpE4CHyXTTU3q5r/tW0dV93bev\nV+5Pr4w3Y7wj6UFgC+GmkV8Af7LwbI2kcN98qZJvlVzBffOmar5Zcr9hKhbMgYQ7wTqATWb2WKoF\n4775UiXfKrmC++ZN1XyzFDXbyYWEM9ZzzKyM28AHi/vmS5V8q+QK7ps3VfMFCpp4RAlOuFAP982X\nKvlWyRXcN2+q5lsjyRmmHMdxnH1HMhOPOI7jOPngFb3jOE6D4xW94zhOg+MVveM4ToPjFb0zJJBk\nku7JpJskvSrpkT38vFZ1T16OpJl7+lmOkzde0TtDhdeBDyrMNwowB/jrXnxeK+GaasdJHq/onaHE\nSuDj8X0H4TGzAEgaLekhSRskrZLUFpdfI2mppKckvSzporjJYuAISesl3RCXtUh6UNIfJN1b1LSB\njtMfXtE7Q4nlwHxJzYTJI1Zn8q4F1plZG7AQuDuTNxloJzzA6muSRgBfBV4ysw+Z2aVxvanAV4Cj\nCfOvTs/zyzjOQPGK3hkymNkGYCKhNb+yR/ZJwLK43hPAGEkHxbxHzewtM9sGbAXG9fEv1pjZ5njn\n5Pr4vxyndIp61o3jpMIK4EZgJjBmgNtkn2mym77jZqDrOU6heIveGWosBa41s+d7LH+aMOkzkmYC\n28zsX3U+59/AyFwMHWcf4y0OZ0hhZpuBJb1kXQMslbSBMMvVuf18znZJz0jaCPyMMF+o4ySJP9TM\ncRynwfGhG8dxnAbHK3rHcZwGxyt6x3GcBscresdxnAbHK3rHcZwGxyt6x3GcBscresdxnAbHK3rH\ncZwG5131vrncbqgAeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "milk.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fxbb9fhP1rsU"
   },
   "source": [
    "### Train Test Split\n",
    "\n",
    "** Let's attempt to predict a year's worth of data. (12 months or 12 steps into the future) **\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "TSYpC-nu1rsW",
    "outputId": "aca7fe1e-412f-4753-9488-2e3c20ad4c87"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 168 entries, 1962-01-01 01:00:00 to 1975-12-01 01:00:00\n",
      "Data columns (total 1 columns):\n",
      "Milk Production    168 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.6 KB\n"
     ]
    }
   ],
   "source": [
    "milk.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "00vc2Jms1rsc",
    "outputId": "d7561011-3b10-4de6-f3ca-b69de5991200"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "168"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(milk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F_PjB7rq1rsf"
   },
   "outputs": [],
   "source": [
    "train_set= milk.head((1976-1962-1)*12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "C6632CCp1rsi"
   },
   "outputs": [],
   "source": [
    "test_set = milk.tail(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_9PtX6Um1rsl"
   },
   "source": [
    "### Scale the Data\n",
    "\n",
    "** Use sklearn.preprocessing to scale the data using the MinMaxScaler. Remember to only fit_transform on the training data, then transform the test data. You shouldn't fit on the test data as well, otherwise you are assuming you would know about future behavior!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "czuAVyRj1rsl"
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UFn7xXzj1rsp"
   },
   "outputs": [],
   "source": [
    "scaler = MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "hu0-OYDX1rsq"
   },
   "outputs": [],
   "source": [
    "train_scaled = scaler.fit_transform(train_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "O2uDgWsI1rsu"
   },
   "outputs": [],
   "source": [
    "test_scaled = scaler.transform(test_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EiI-RuSPKC7v"
   },
   "source": [
    "## Build Training Data(many to many)\n",
    "\n",
    "* X_train: Past 12 monthes productions, shape: (-1,12)\n",
    "* Y_train: Future 12 month productions, shape:(-1, 12) \n",
    "* Shift the window to get more trainning data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6YaF3wjKKB4Y"
   },
   "outputs": [],
   "source": [
    "def build_train_data(data, past_monthes = 12, future_monthes = 12):\n",
    "  X_train, Y_train = [],[]\n",
    "  \n",
    "  for i in range(data.shape[0] + 1 - past_monthes - future_monthes):\n",
    "    X_train.append(np.array(data[i:i + past_monthes]))\n",
    "    Y_train.append(np.array(data[i + past_monthes:i + past_monthes + future_monthes]))\n",
    "    \n",
    "  return np.array(X_train).reshape([-1,12]), np.array(Y_train).reshape([-1,12])\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "t_z8xNdaQjvH"
   },
   "outputs": [],
   "source": [
    "x, y = build_train_data(train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "DiLmBOy4Qq3j",
    "outputId": "4dcf70c3-8d63-4e4c-b4fa-b861e86afc61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x shape; (132, 12) y shape:  (132, 12)\n"
     ]
    }
   ],
   "source": [
    "print('x shape;', x.shape, 'y shape: ', y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JxbPdOLds9JD"
   },
   "source": [
    "Take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "3_tRprRSjkYY",
    "outputId": "42d53ef8-7601-490f-bdc1-00a525579c3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f64a50b6ba8>]"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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uC8szjfpC32fM1nw7v3BsgKJKkSzhKAW55j/YlrdBNes3nk48mcPQ99cze/1R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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x[12])\n",
    "plt.plot(y[12])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "P_rsY5NQQ3op"
   },
   "source": [
    "## Keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1GiFVMqlQ3or"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras import layers, Sequential,models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AsBnLuapQ3ot"
   },
   "outputs": [],
   "source": [
    "RNN_CELLSIZE = 10\n",
    "SEQLEN = 12\n",
    "BATCHSIZE = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "j96G7Ef0k5Z-"
   },
   "source": [
    "### Build and Train the Model\n",
    "Many to Many\n",
    "Stateful is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 306
    },
    "colab_type": "code",
    "id": "vwLHV-p5k5Z0",
    "outputId": "12201115-15fc-4936-ebf1-72216353d729"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "reshape_3 (Reshape)          (1, 12, 1)                0         \n",
      "_________________________________________________________________\n",
      "gru_6 (GRU)                  (1, 12, 10)               360       \n",
      "_________________________________________________________________\n",
      "gru_7 (GRU)                  (1, 12, 10)               630       \n",
      "_________________________________________________________________\n",
      "time_distributed_3 (TimeDist (1, 12, 1)                11        \n",
      "_________________________________________________________________\n",
      "flatten_3 (Flatten)          (1, 12)                   0         \n",
      "=================================================================\n",
      "Total params: 1,001\n",
      "Trainable params: 1,001\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_layers = [\n",
    "    layers.Reshape((SEQLEN,1),input_shape=(SEQLEN,),batch_size = BATCHSIZE),\n",
    "    layers.GRU(RNN_CELLSIZE, return_sequences=True, stateful=True),\n",
    "    layers.GRU(RNN_CELLSIZE, return_sequences=True, stateful=True),\n",
    "    layers.TimeDistributed(layers.Dense(1)),\n",
    "    layers.Flatten()\n",
    "    \n",
    "]\n",
    "model = Sequential(model_layers)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qU4zPwkzk5Zt"
   },
   "outputs": [],
   "source": [
    "model.compile(\n",
    "       loss = 'mean_squared_error',\n",
    "       optimizer = 'adam'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3417
    },
    "colab_type": "code",
    "id": "6AW4Vl5-k5ZP",
    "outputId": "f73b73a3-828c-43f1-f18b-67eb82c96ad5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "132/132 [==============================] - 7s 55ms/sample - loss: 0.0563\n",
      "Epoch 2/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0218\n",
      "Epoch 3/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0134\n",
      "Epoch 4/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0068\n",
      "Epoch 5/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0039\n",
      "Epoch 6/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0028\n",
      "Epoch 7/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0024\n",
      "Epoch 8/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0021\n",
      "Epoch 9/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0022\n",
      "Epoch 10/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 11/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0021\n",
      "Epoch 12/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 13/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0023\n",
      "Epoch 14/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 15/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 16/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0021\n",
      "Epoch 17/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 18/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 19/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 20/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 21/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 22/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 23/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 24/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 25/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 26/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0023\n",
      "Epoch 27/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 28/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 29/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0020\n",
      "Epoch 30/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0019\n",
      "Epoch 31/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0021\n",
      "Epoch 32/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0017\n",
      "Epoch 33/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 34/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0021\n",
      "Epoch 35/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0021\n",
      "Epoch 36/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 37/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0020\n",
      "Epoch 38/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0016\n",
      "Epoch 39/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 40/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 41/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 42/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0020\n",
      "Epoch 43/100\n",
      "132/132 [==============================] - 6s 49ms/sample - loss: 0.0018\n",
      "Epoch 44/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 45/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0020\n",
      "Epoch 46/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 47/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 48/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 49/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 50/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0016\n",
      "Epoch 51/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0021\n",
      "Epoch 52/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 53/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 54/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 55/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0019\n",
      "Epoch 56/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 57/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 58/100\n",
      "132/132 [==============================] - 6s 49ms/sample - loss: 0.0017\n",
      "Epoch 59/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 60/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 61/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0016\n",
      "Epoch 62/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 63/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 64/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 65/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 66/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 67/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 68/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0017\n",
      "Epoch 69/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0016\n",
      "Epoch 70/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 71/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 72/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0019\n",
      "Epoch 73/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0016\n",
      "Epoch 74/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0016\n",
      "Epoch 75/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 76/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 77/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0019\n",
      "Epoch 78/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0017\n",
      "Epoch 79/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0018\n",
      "Epoch 80/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 81/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 82/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 83/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 84/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 85/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 86/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 87/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0016\n",
      "Epoch 88/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 89/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0017\n",
      "Epoch 90/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 91/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 92/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 93/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0017\n",
      "Epoch 94/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 95/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0016\n",
      "Epoch 96/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0016\n",
      "Epoch 97/100\n",
      "132/132 [==============================] - 6s 47ms/sample - loss: 0.0018\n",
      "Epoch 98/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0017\n",
      "Epoch 99/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0017\n",
      "Epoch 100/100\n",
      "132/132 [==============================] - 6s 48ms/sample - loss: 0.0017\n"
     ]
    }
   ],
   "source": [
    "h = model.fit(x,y, \n",
    "              epochs = 100)\n",
    "\n",
    "#model.save('gdrive/My Drive/dataML/rnn_many_to_many_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "bMwy2IIozaRt",
    "outputId": "154d67af-8d56-4ad8-faaa-bd99cc12f624"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f6455a9e7f0>]"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vwEcpL+UNEdtvM2sFPkz5anfcPefuY8TgWFNeKaA+XMerAThFBI+1u/895RUM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\nRGJEoS8iEiMKfRGRGFHoi4jEyP8Hi+YFqQ/U4IsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(h.history['loss'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "r714-jBM1Yo-"
   },
   "source": [
    "### Predict Future \n",
    "* Use the last 12 monthes data of the train data set to predict the future 12 monthes production \n",
    "* Compare the predicted data with the real data in test data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BnGC-2ohfLwz"
   },
   "outputs": [],
   "source": [
    "#model = models.load_model('gdrive/My Drive/dataML/rnn_many_to_many_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QkYcxMMgqGN0"
   },
   "source": [
    "Get the last 12 monthes data of the train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ncuo3aL1z3K_"
   },
   "outputs": [],
   "source": [
    "last_12_month_data = train_scaled[-12:].reshape([1,12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5_sSDDnY0P1t"
   },
   "outputs": [],
   "source": [
    "predicts = model.predict(last_12_month_data).reshape([12,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y4DPuuxq0YmI"
   },
   "outputs": [],
   "source": [
    "results = scaler.inverse_transform(np.array(predicts).reshape(12,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119
    },
    "colab_type": "code",
    "id": "h1-x_pW71iM_",
    "outputId": "6b20330a-3684-4fb5-f3a4-9ad7c3344d8e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "test_set['Generated'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 452
    },
    "colab_type": "code",
    "id": "xR3TovJi1iNF",
    "outputId": "b66a582a-7330-4c25-9521-4c464fa26b53"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Milk Production</th>\n",
       "      <th>Generated</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01 01:00:00</th>\n",
       "      <td>834.0</td>\n",
       "      <td>849.470032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-02-01 01:00:00</th>\n",
       "      <td>782.0</td>\n",
       "      <td>799.586792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-03-01 01:00:00</th>\n",
       "      <td>892.0</td>\n",
       "      <td>901.940552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-04-01 01:00:00</th>\n",
       "      <td>903.0</td>\n",
       "      <td>911.778137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-05-01 01:00:00</th>\n",
       "      <td>966.0</td>\n",
       "      <td>966.672180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-06-01 01:00:00</th>\n",
       "      <td>937.0</td>\n",
       "      <td>944.206604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-07-01 01:00:00</th>\n",
       "      <td>896.0</td>\n",
       "      <td>908.457214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-08-01 01:00:00</th>\n",
       "      <td>858.0</td>\n",
       "      <td>868.784302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-09-01 01:00:00</th>\n",
       "      <td>817.0</td>\n",
       "      <td>820.643433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-10-01 01:00:00</th>\n",
       "      <td>827.0</td>\n",
       "      <td>815.145508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-11-01 01:00:00</th>\n",
       "      <td>797.0</td>\n",
       "      <td>780.117981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-12-01 01:00:00</th>\n",
       "      <td>843.0</td>\n",
       "      <td>817.687866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Milk Production   Generated\n",
       "Month                                           \n",
       "1975-01-01 01:00:00            834.0  849.470032\n",
       "1975-02-01 01:00:00            782.0  799.586792\n",
       "1975-03-01 01:00:00            892.0  901.940552\n",
       "1975-04-01 01:00:00            903.0  911.778137\n",
       "1975-05-01 01:00:00            966.0  966.672180\n",
       "1975-06-01 01:00:00            937.0  944.206604\n",
       "1975-07-01 01:00:00            896.0  908.457214\n",
       "1975-08-01 01:00:00            858.0  868.784302\n",
       "1975-09-01 01:00:00            817.0  820.643433\n",
       "1975-10-01 01:00:00            827.0  815.145508\n",
       "1975-11-01 01:00:00            797.0  780.117981\n",
       "1975-12-01 01:00:00            843.0  817.687866"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 298
    },
    "colab_type": "code",
    "id": "6y5o6kV21iNN",
    "outputId": "8fe7f359-fb6e-4d2f-9994-bcbd2674bf15"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6455b67080>"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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327hxI4cOHbr288WLF8nKymLbtm388ccfANx33300bNiwxOurM70SvZSyRwnHSm3aLKWc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\njjt6x3GcyPl/70LVjxM6ZLgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_set.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WkrgRIqzkXgq"
   },
   "source": [
    "### Predict more \n",
    "Use the first months data to predict the future 13 years' milk productions and compare with the real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "sbRNA4ERklYA"
   },
   "outputs": [],
   "source": [
    "train_seed = list(train_scaled[:12].flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "F91e0IzvklYE"
   },
   "outputs": [],
   "source": [
    "def get_prediction(data_list):\n",
    "  train_seed = data_list\n",
    "  for i in range(13):\n",
    "    x_train = np.array(train_seed[-12:]).reshape(1,12)\n",
    "    one_predict = model.predict(x_train)\n",
    "    train_seed.extend(one_predict.flatten().tolist())\n",
    "   \n",
    "  return train_seed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BNW_8HJ2klYG"
   },
   "outputs": [],
   "source": [
    "train_seed = get_prediction(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "TvgurkwHklYI",
    "outputId": "3fd521ef-aee8-4ce2-ce3e-f60c4b931012"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f64559d3550>]"
      ]
     },
     "execution_count": 50,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zddoMpGW1gcuLRdcDUkDn/oDQ4B/NJ5CKhvAbb78bo5nOm7nauf1QBn/1zvtQ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\niAMCBXyCIIgDAgV8giCIA8L/D9Tintm336YsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(train_seed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pcNqRCbiklYN"
   },
   "outputs": [],
   "source": [
    "results = scaler.inverse_transform(np.array(train_seed).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K-8x-sOFmBav"
   },
   "outputs": [],
   "source": [
    "milk_predict = milk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "387zn4SBmKsl"
   },
   "outputs": [],
   "source": [
    "milk_predict['Generated'] = results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 290
    },
    "colab_type": "code",
    "id": "PDvQXKtjklYX",
    "outputId": "d2d47f15-dd9e-459e-f479-e0426a44a7d3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f6455a085c0>"
      ]
     },
     "execution_count": 54,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4h2T8wZr5RO+PddNGhvFUbXdPfj2LjmtWjPAshKTdtEtJVR0JUSiVaSGPvhTR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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "milk_predict.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zIAwIfd6JDa8"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "name": "3_RNN_Many_to_Many_Stateful_Keras.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
